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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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LICS:为多语言场景文本检测定位字符间空间.

Po-Chyi Su1, Meng-Chieh Lee1, Yi-Ting Tung1

  • 1Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan.

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概括
此摘要是机器生成的。

本研究介绍了定位字符间空间 (LICS),这是一种用于多语言场景文本检测的新方法. LICS有效地识别了字符之间的语言不可知差距,在不同的环境中提高了检测准确性.

关键词:
角色的差距是因为角色的差距.字符识别功能 字符识别功能深度学习是一种深度学习.多语言文本本地化本地化现场文本检测 现场文本检测语义细分 语义细分 语义细分 语义细分缺乏监督的学习学习.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 多语言场景文本检测面临的挑战是由于语言特定的特性和广泛的数据要求.
  • 传统的方法在自然场景中与各种角色形状,方向,变形和遮蔽作斗争.

研究的目的:

  • 为可行的多语言场景文本检测开发一种语言不可知的方法.
  • 为了减少新语言中场景文本检测的注释负担.

主要方法:

  • 介绍 定位字符间空间 (LICS),检测语言无关的字符间差距.
  • 采用了两阶段的方法:合成数据的培训与空白注释,其次是对真实数据的弱监督学习与字级标签.
  • 引入了标记字符的街景文本 (CSVT) 数据集与标准化的注释原则.

主要成果:

  • LICS表现出强的表现,特别是在亚洲脚本,ICDAR和全文基准上.
  • 缺乏监督的学习框架消除了对目标语言字符级注释的需求.
  • CSVT数据集为多语言场景文本分析研究提供了宝贵的资源.

结论:

  • LICS为多语言场景文本检测提供了强大而高效的解决方案.
  • 拟议的弱监管方法显著降低了注释成本.
  • 该CSVT数据集将推动多语言场景文本理解的研究.